In many spatio-temporal data, their spatial variations have inherent global and local structures. The spatially continuous dynamic factor model (SCDFM) decomposes the spatio-temporal data into a small number of spatia...
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In many spatio-temporal data, their spatial variations have inherent global and local structures. The spatially continuous dynamic factor model (SCDFM) decomposes the spatio-temporal data into a small number of spatial and temporal variations, where the spatial variations are represented by the factor loading (FL) functions. However, the FL functions estimated by the maximum likelihood or maximum L-2 penalized likelihood capture global structures but do not capture local structures. We propose a method for estimating the spatially multi-scale FL functions using a sparse penalty. To overcome the problems of existing sparse penalties, we propose the adaptive graph lasso (AGL) penalty. The method with the AGL penalty eliminates redundant basis functions contained in the FL functions, and leads to the FL functions having global and localized structures. We derive the em algorithm with block coordinate descent that enables us to maximize the AGL penalized log-likelihood stably. Applications to synthetic and real data show that the proposed modeling procedure accurately extract not only the spatially global structures but also spatially local structures, which the L-2 penalized estimation do not extract.
In the stochastic volatility framework of Hull and White (1987), we characterize the so-called Black and Scholes implied volatility as a function of two arguments: the ratio of the strike to the underlying asset price...
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In the stochastic volatility framework of Hull and White (1987), we characterize the so-called Black and Scholes implied volatility as a function of two arguments: the ratio of the strike to the underlying asset price and the instantaneous value of the volatility. By studying the variations in the first argument, we show that the usual hedging methods, through the Black and Scholes model, lead to an underhedged (resp. overhedged) position for in-the-money (resp. out-of-the-money) options, and a perfect partial hedged position for at-the-money options. These results are shown to be closely related to the smile effect, which is proved to be a natural consequence of the stochastic volatility feature. The deterministic dependence of the implied volatility on the underlying volatility process suggests the use of implied volatility data for the estimation of the parameters of interest. A statistical procedure of filtering (of the latent volatility process) and estimation (of its parameters) is shown to be strongly consistent and asymptotically normal.
This paper presents an easy-to-compute semi-parametric (SP) method to estimate a simple disequilibrium model proposed by Fair and Jaffee (1972). The proposed approach is based on a non-parametric interpretation of the...
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This paper is concerned with the statistical analysis of proportions involving extra‐binomial variation. Extra‐binomial variation is inherent to experimental situations where experimental units are subject to some s...
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A hierarchical latent regression model is suggested to estimate nested and nonnested relationships in complex samples such as found in the National Assessment of Educational Progress (NAEP). The proposed model aims at...
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Maximum likelihood estimation with nonnormal error distributions provides one method of robust regression. Certain families of normal/independent distributions are particularly attractive for adaptive, robust regressi...
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Well-known numerical integration methods are applied to item response theory (IRT) with special emphasis on the estimation of the latent regression model of NAEP. An argument is made that the Gauss-Hermite rule enhanc...
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In standard particle filter (PF), the principal problem which influences the estimation performance is sample depletion brought by resampling step. To solve the problem, this paper presents an improved PF algorithm, t...
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In standard particle filter (PF), the principal problem which influences the estimation performance is sample depletion brought by resampling step. To solve the problem, this paper presents an improved PF algorithm, the posterior state density that represented by a Gaussian mixture model is recovered from the particle set of the measurement update step by means of a weighted Expectation-Maximization (em) algorithm. This step replaces the resampling stage needed by most particle set and reduced computational complexity compared to other related algorithms. It is demonstrated by simulation that this new approach has an improved estimation performance and reduced computational complexity compared to other related algorithms.
Lots of deferent ways can be used to mine outliers, among which the forward search algorithm is one of the most important ways. Since data are incomplete, data mining for outliers will encounter some difficulties, and...
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Lots of deferent ways can be used to mine outliers, among which the forward search algorithm is one of the most important ways. Since data are incomplete, data mining for outliers will encounter some difficulties, and thus one needs to make an attempt on this field. First of all, one should think of the fill of those lost data. Thinking of the mixed loss, one can simplify the application of algorithm, such as em algorithm and MI algorithm. Furthermore, the more simple and facile RE algorithm is proposed. The actual fill of data indicates the effect of the method. When one uses the forward search algorithm to mine outliers, analyzing the formation of em algorithm, he can use the same method to estimate the unknown parameter. Even when making usual statistical outliers testing, the test statistics that relies on residuals can also be also generated by em algorithm. That means the result of data mining is more credible when one first completes and then mines the data. Finally, if one clusters the data before he selects initial subset, the result of research can be better and faster. What' s more, false conclusion can be avoided.
Analysis of independent components, as a new signal processing tool, its application in seismic signal processing region has been preliminarily discussed by many scholars. Based on adaptive Gauss hybrid blind deconvol...
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Analysis of independent components, as a new signal processing tool, its application in seismic signal processing region has been preliminarily discussed by many scholars. Based on adaptive Gauss hybrid blind deconvolution presented by Mr. San-tamaria et al and in combination with Bussagang deconvolution algorithm in analysis of independent components, the paper improved negative entropy rule-based objective function by introducing constraint item and constructed new non-linear operator;meanwhile, using the updated expected maximization (em) algorithm to optimize the parameters of Gauss hybrid model of reflection coefficient, successfully implementing the blind deconvolution of seismic signals. The results of numeric simulation and practical data processing showed that the approach could be better suitable for non-minimum phase system, obtain the optimum evaluation of raw reflection coefficient and be characteristic of fast convergence and high precision, which is effective tool for improving the resolution of seismic data.
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